#azure ai tutorial

[rajaniesh]

Empowering Your Business with AI: Building a Dynamic Q&A Copilot in Azure AI Studio

In the rapidly evolving landscape of artificial intelligence and machine learning, developers and enterprises are continually seeking platforms that not only simplify the creation of AI applications but also ensure these applications are robust, secure, and scalable. Enter Azure AI Studio, Microsoft’s latest foray into the generative AI space, designed to empower developers to harness the full…

[ibarrau]

[DAX] Descripciones en medidas con Azure Open AI

Hace un tiempo lanzamos un post sobre documentar descripciones de medidas automaticamente usando la external tool Tabular Editor y la API de ChatGPT. Lo cierto es que la API ahora tiene un límite trial de tres meses o una cantidad determinada de requests.

Al momento de decidir si pagar o no, yo consideraría que el servicio que presta Open AI dentro de Azure tiene una diferencia interesante. Microsoft garantiza que tus datos son tus datos. Qué lo que uses con la AI será solo para vos. Para mi eso es suficiente para elegir pagar ChatGPT por Open AI o por Azure. 

Este artículo nos mostrará como hacer lo que ya vimos antes pero deployando un ChatGPT 3.5 y cambiando el script de C# para utilizar ese servicio en Azure.

Para poder realizar esta práctica necesitamos contar con un recurso de Azure Open AI. Este recurso se encuentra limitado al público y solo podremos acceder llenando una encuesta. Fijense al momento de crear el recurso debajo de donde seleccionaríamos el precio.

La respuesta de Microsoft para permitirnos usar el recurso puede demorar unos días. Una vez liberado nos permitirá usar un Tier S0. Este recurso es un espacio que nos permite explorar, desarrollar, deployar modelos. En nuestro caso queremos deployar uno ya existente. Al crear el recurso veremos lo siguiente y antes de ingresar a Deploy, copiaremos valores de interes.

Para nuestro script vamos a necesitar el “Endpoint” y una de las “Keys” generadas. Luego podemos dar click en “Deploy”.

Al abrir Azure AI Studio vamos a “Deployments” para generar uno nuevo y seguimos esta configuración:

El nombre del deploy es importante puesto que será parte de la URL que usamos como request. Seleccionen esa versión de modelo que se usa para Chat especificamente de manera que repliquemos el comportamiento deseado de ChatGPT.

Atención a las opciones avanzadas puesto que nos permiten definir la cuota de tokens por minuto y el rate limiting de requests por minuto. Para mantenerlo similar a la API gratuita de Open AI lo puse en 30. Son 10 más que la anterior. 

NOTA: ¿Por qué lo hice? si ya intentaron usar la API Trial de GPT verán que les permite ver sus gastos y consumos. Creo que manteniendo ese rate limiting tuve un costo bastante razonable que me ayudó a que no se extienda demasiado puesto que no solo lo uso para descripciones DAX. Uds pueden cambiar el valor

Con esto sería suficiente para tener nuestro propio deploy del modelo. Si quieren probarlo pueden ir a “Chat” y escribirle. Nos permite ver requests, json y modificarle parámetros:

Con esto ya creado y lo valores antes copiados podemos proceder a lo que ya conocemos. Abrimos PowerBi Desktop del modelo a documentar. Luego abrimos Tabular Editor y usaremos el siguiente Script para agregar descripciones a todas las medidas que no tengan descripción previa. Tiene un pausador al llegar a 30 porque es el rate limiting que yo definí en mi modelo. Eso pueden cambiarlo

Las primeras variables son los valores que copiamos y el nombre del deployment. Completando esos tres el resto debería funcionar. El script lo pueden copiar de mi Github.

Con esto obtendrán las descripciones de las medidas automaticamente en sus modelos utilizando el servicio de Azure Open AI, espero que les sirva.

[dawnian-janitor]

Let's Talk about Deepseek

So, obviously I'm bad at using Tumblr. Sorry about that...

But I wanted to share my experiences with Deepseek. Long post as it includes tutorials and stuff. Below the cut.

My Thoughts

Deepseek R1 is very smart but oddly gets too poetic for me. To the point it becomes unusable, personally.

Deepseek V3 is less smart than R1, but smarter than JLLM. It also does not have the reasoning stuff R1 is known for.

JLLM is ass. Point blank.

Proxies & Cost

Proxies are always on with my bots because I see no reason not to. With the JLLM being so bad these days I recommend using a proxy, anyway. That said, I know that some cost.

Deepseek R1 and Deepseek V3 are currently free to use, so I do recommend using them.

How To Use

Here is a Reddit post about how to set up R1 as your proxy. Edit: That link above is to use Deepseek PAID. This link is to use it free. Sorry!

If you get the bot's reasoning to their reply and dislike it, go to your open router ai settings and block TARGON.

If you get a lot of loading errors, I have seen people suggest blocking CHUTES.

This leaves you with only AZURE. For me personally, blocking CHUTES has caused way more issues.

If you want to use V3 instead, you follow the same steps as in the link above, only with V3 (free). In Janitor's proxy settings, change the API Key to the one generated for V3, and the model to "deepseek/deepseek-chat:free". Refresh page/fully exit Janitor and re-enter for it to work.

I still have TARGON blocked for this, but I've heard it does not cause logics problems with V3.

I do not have CHUTES blocked for this, and do not receive errors.

Summary + Recommendations

JLLM is great with sex scenes. Deepseek seems to stumble and flail (V3, specifically) as it gets caught up on consent, even if you have already given it. JLLM is great at moving the scene forward and getting creative.

However, JLLM is awful with memory and is just plain stupid. For literally every other interaction, Deepseek is better.

Because of this, I highly encourage alternating between JLLM and Deepseek to get the most out of your roleplay.

Start with whichever LLM you prefer. Once it grows stupid (begins to forget, gets caught on certain phrases, etc), change it.

Utilize the Chat Memory feature. Auto-summary has been broken for a while with JLLM, and I haven't tried it with Deepseek. I recommend manually writing a summary or bullet points only of the really important stuff. (Deepseek has a significantly higher context, but if you alternate, JLLM is still shockingly low).

Personally, use JLLM for romantic/sexual scenes, and Deepseek for literally everything else, but use your preference however you like.

Janitor has built-in proxy prompts to choose from. "Freedom" is mostly for OpenAI. "Erotica" and "Romance" work decently with Deepseek.

Important Notices

I'm sure most people who are aware of Deepseek know this already, but just in case: Deepseek is a Chinese company, and some think there are security worries due to potential connections to the Chinese government. I don't know shit about anything like this or that, so I genuinely do not know. That said I do encourage you to read up on it if you are worried at all. I, personally, do not care that much.

[sketchfanda]

Kirishima's Mystique:Virtual Sensuality

In the cozy little apartment that our resident Hero College trio shared and called their love-nest, Kirishima eyed the device he held in his hand before him. It was his birthday and of course Mina and Maya seemed to always go all out as he examined the gift they'd given him. Some sort of headgear or headset device, like those old virtual reality goggles as they bore the distinct logo indicating they'd been made on and came from none other than I-Island. Looking at his grinning girlfriends who seemed very eager no doubt to see what he thought and if he was going to try them on.

Kirishima:"Okay so what exactly is this?" *You couldn't lame him for being curious and wary. For one they already ha plenty of games consoles and two? Mina and Maya usually liked to be more saucier and spicier when it came to his birthday. Like the time they arranged that little weekend in Maya's home in Jagua or the time they got him a stripper.*

Mina:"That oh sweet man of ours is a very special gift custom made and ordered by the lovely Melissa Shield back on I-Island. As you might gather it's a virtual reality headset but this one's very special. It's total sensory immersion.*The bubblegum duo couldn't help but giggle at the adorable puzzled look on his face. No doubt he didn't have a clue what that meant and really could they blame their himbo? It took a while for them to get the gist of it when Melissa explained it. But once they got an understanding, well..*

Maya:"Meaning handsome you're not whole being is uploaded into the game. It's got a few devices in there keeps your body safe and sound while giving you just the right feedback. It'll make you literally feel like you're in the game and believe you us, stud, you're gonna love this game."*the shapeshifting bombshell added as she provided enough context. Yes it was exactly as said, soon as Kirishima put that headset on and it powered up, he'd find his 5 senses all experiencing a whole new world. One they were sure he was going to enjoy especially thanks to the customising and modifications Melissa made. That girl definitely earned a solid from them that's for sure.*

Well when they put it like that of course, Kirishima didn’t see any reason to doubt them, shrugging as he put on the VR headset. Taking a seat on the couch as he felt his body go into a relaxed state as his gift powered on, it was almost like drifting off into a dream. Closing his eyes briefly before opening them to find himself no longer in his living room but rather a sort of limbo. All around him from above and below to the sides, front and back was a clear blue sky with stray white clouds gently floating in the breeze and the floor a calm gentle ocean reflecting it like a mirror.

Kirishima took in the scenery around him, practically feeling a warmth akin to summer and smelling the fresh air. This really was like being in a whole other world but how exactly did this game work? Was there a tutorial or the like? What was he meant to do next?

??:"That's where I come in...."*The sturdy himbo flinched, jumping a bit as he turned to face the source of the voice that had suddenly spoken up. Seeing a blue glowing ghost-like woman levitating before him and he could only that damn she was quite the looker. A curvy toned body seemingly nude with her modesty preserved by digital patterns akin to that old Disney movie Tron.*"Hello there, pardon the sudden shock, welcome to Oasis. I'm Cortana, your admin AI and general tutorial guide and instructor. here to oversee your enjoyment and time here with the game. Nothing too major or serious, you play it however you want to. Just think it and it will happen, want to be a dragon in a fantasy world? Then go for it, maybe you want to be a pro-wrestler or a shark? Hunt down dinosaurs? All possible for you and of course the time you spend here amounts to only seconds back out in the real world..."*The azure digital babe spoke gently yet matter of fact-like, demonstrating an example of the VR world's potential as she held out her hand and made a single lotus flower bloom in her palm.*

Kirishima:*briefly shook his head as well as calmed down that voice in his brain embodying his libido. No doubt stirred by the sight of the more than easy on the eyes AI girl as he thought her words over.*"Anything anyway huh?"*He'd mused as he looked out at the tranquil limbo around him, which within seconds became replaced by a perfect replica forming of UA campus, from the school to the gymnasium and Heights Alliance dorm. In awe at how quickly the game played to his thoughts all the Cortana simply smiled...a distinct little mischievous twinkle in her eyes.*

Suffice to say that it wasn't long before Kirishima was giving Oasis a whirl, trying out being a shark as he swam in the ocean or took to the sky flying as a dragon. It was surreal for sure and felt incredible while Cortana maintained vigilance, no doubt ensuring his overall health and well being here in the virtual world. Time truly flied as he had fun as he caught his breath, relaxing on a simple, calm and tranquil grass field, Cortana sitting beside him cool, calm and composed as ever. Her digital form giving off quite a nice calm ethereal glow to it as she found the sturdy himbo look her way.

Kirishima:”Hey this has been great so far and all but if you don’t mind me asking, what would be fun for you? I mean if you got any ideas or anything, I wouldn’t mind trying them out.”*Asking sincerely as he was curious to see if the AI girl was willing to do more than just observe and ensure his own enjoyment of Oasis. Little did he know of course the moment he asked that question, was just what Cortana had been waiting for. Her azure eyes twinkling as she grew a playful little smile. One that reminded him of the kind Mina and Maya would give him during…..”certain” times.*

Cortana:”Well as a matter of fact…”*The admin AI's grin widened with sensual delight as with a snap of her fingers and a wave of her hand, the scenery around them changed. Oh how familiar it seemed to Kirishima as it bore a resemblence to one of the pleasure suits of Ms.Midnight's love hotel (that used to be a brothel of course, they didn't call her the rated R heroine for nothing). The queen sized bed, the luxury bathroom and the lush view of the skyline, all identical. Cortana taking delight in his reaction as she could tell his brain was working out the mental gymnastics*

Kirishima:*blushing even as he put the dots together.*"So I'm guessing this is the actual surprise part of my present here huh?"*The sturdy himbo got his answer of course in the form of Cortana’a playful smile as with another gesture, the lines in her body reformed in a different pattern. Leaving her torso utterly exposed before him, naked and ready to give herself to him. Her graceful feet nearly gliding along the floor as he found her pressing herself against him. Arms draping around his powerful shoulder as she pulled him in, pressing her lips to his. For a virtual girl, she sure felt and tasted damn well real enough.*

Cortana:*moaning deeply into the make out she initiated, purring as she felt Kirishima respond by grabbing and squeezing her indigo booty. Her tits rubbing against his firm pecs with delicious friction before she soon broke their liplock. A strand of saliva between them as she panted sensually, eyes twinkling with mischief.*”Ms.Shield was given very thorough instructions and details from your two lover girls in regards to my personality and programming. So feel free to be as rough as you’d like with me….”*With thtat said, she snapped her fingers as Kirishima found his clothes disappear in a flash of pixels, as the AI girl gently nudged him to fall and lay back in the bed.*

The indigo babe licked her lips at the sight of Kirishima in the nude, drinking in his sculpted, training enhanced muscles but especially his rising cock. That length and girth stirring before her to salute her as she climbed onto the bed, straddling his waist as her luscious digital booty hotdogged his sex-meat. Purring as she caressed his chest and abs while leaning down to kiss him once again, as they began to make out and indulge in a little heavy petting. Tongues dancing together as Cortana began to display the amount of sexual knowledge and experience she’d been provided in her programming first hand.

Deep throated came from The Indigo AI babe particularly as she felt Kirishima’s hands grab and squeeze her luscious, bubbly ass his cock pulsed and twitched. Her meaty buns feeling the raw hot desire coming from that length as her virtual pussy began to grind against it. Warm, sticky nectar coating his shaft in a soothing lubricant shower as her tits massaged his strong chest. The AI admin and her player soon rolling around the bed, deepening and intensifying their makeout as their combined arousal skyrocketed.

It wasn’t long before their movements soon came to find them in a 69 position together, occasionally shifting on their sides to one laying atop the other. Cortana moaning with wanton ecstasy as she was performing stunning fellation on Kirishima’s cock, her virtual body not bound by limits snd therefore able to deepthroat him with no problem. Head bobbing away lewdly in sync with the penetrative plunging of his tongue deep into the folds of her slit as he lapped away and drank up her flowing nectar. The sturdy himbo experiencing first hand that sex in this virtual world was just as much a thrilling sensation here as it was back in the real world, Melissa definitely knew her programming.

But as delightfully erotic and pleasurable as the foreplay was, the moment Cortana had that length and girth of his penetrate her was when things really got fun. The indigo digital goddess moaning and gasping passionately as she rode Kirishima cowgirl style, his hands grasping where her waist met and connected with her hips as she found his red eyes glued to her bouncing tits. Her asscheeks clapping snd jiggling with every impact of their loins engaging in the primal dance men and women have done since the dawn of time. And loving every second of it, there was no doubt as much as sexual knowledge had been input into her data, actually experiencing the act of sex itself was even better.

it’d helped that Kirishima was a more than willing and able partner of course but given the intel she’d been provided about him, that wasn’t surprising. Nor was he content to simply stay idle and let her do all the work as he proceeded to assert dominance and show the blue bombshell first hand his depth of sexual prowess. Pouncing on her as she found herself in a mating press, screaming with ecstasy that is when she wasn’t making sweet sloppy tongue kisses with the stud jackhammering away into her. Orgasms rocking her digital form before she found it only getting better from here on in.

Doggy style, spread eagle, full nelson and more as the thicc toned virtual babe was out through a kama sutra marathon by the Herculean himbo. To say nothing of the delight when she got anal, it certainly helped that her own sexual sensitivity had been tinkered with enough that anal and oral sex wasn’t just in par with getting her pussy hammered, her entire body was one whole erogenous zone. One that the red riot stallion couldn’t get enough of as he couldn’t help but lose track of time. Minutes passing into hours within Oasis which amounted to just a few seconds back in reality which meant the lair could have themselves a marathon.

Which they most certainly did of course and Cortana could only imagine how much fun her player would have once they went from this bedroom to more exotic locations like the beach. Or especially when the next level of his surprise would make itself known soon as they decided to join in in the game. But no rush of course, they had all the time in the world. As back in the real world, Kirishima was laying back on the couch, headset on and a blissful smile on his face as his girlfriends sat on either side of him.

Maya:”Looks like he’s having fun but thst pats to be expected….so Pinky resdy to make this a 3 player game?”*The shapeshifting bombshell grinned at Mina as they both held up their own copies of the VR headset. Of course Melissa was kind enough to provide them with the means to connect with their boyfriend and enjoy Oasis together. They more than owed that blonde a solid that’s for sure. Nothing says happy birthday boyfriend like this….until they decided to try and top it down the line.

Mina:Ooh you know it…”*The bubblegum acid babe quipped in reply, the duo sharing a kiss as they began to slide their headgears on. It wasn’t long before they were soon laying beside their man and joining him in making his birthday present even more fun and spicier than it already was. Making this a very happy birthday for their Red Riot and making sure Melissa got some very well earned reviews…*

[hiringiosdevelopers]

Guide to Hire Artificial Intelligence Developers

The Booming Demand for AI Professionals

The artificial intelligence industry has expanded at breakneck speeds over the past few years, presenting new developers with more opportunities than ever. Transitions into careers as AI developers can seem daunting in the presence of the enormity and pace of industry changes. Familiarity with the landscape as well as a thoughtfully planned approach can ease the transition for new hire artificial intelligence developers into a fulfilling yet demanding profession.

Creating Strong Mathematical Foundations

Mathematics is the backbone to building artificial intelligence. Future artificial intelligence developers would be well advised to possess extensive working knowledge of statistics, linear algebra, and calculus. Mathematical concepts are not pedantic principles,these are working tools developers use day in and day out to comprehend algorithms, tune models, and solve complex problems.

The good news is that math is learned in stages. New AI developers need not know each piece of math before starting, yet must be ready to learn on a daily basis. Online tutorials, books, and project learning are excellent vehicles for locking down math concepts as well as learning by doing.

Choosing the Right Programming Languages

Programming skill is necessary for any artificial intelligence developer, but the proper selection of languages will accelerate career growth. Python is currently the most popular language in AI development due to its vast collection of libraries and simplicity of use. R is needed for statistical data and data science, whereas Java and C++ are essential in production environments.

New AI developers will start with Python and later develop language proficiency over time based on career goals. A basic understanding of at least one language and experience with the relative advantages and applications of others is the greatest concern. There is more doing than talking about things that should be done, so project work is essential to programming skill acquisition.

Acquiring Pragmatic Experience Through Projects

Practical experience is in great demand among the upcoming generation of artificial intelligence coders. Side projects demonstrate skill to potential employers and permit learning new tools and techniques. Starting with simple projects and progressing to more complex ones develops confidence and skill.

Good starting projects would include image classification software, sentiment analysis software, or recommendation software. The plan is to choose projects that fit career goals but also cover various aspects of AI development. Documentation of projects and publishing on platforms like GitHub is a way of showcasing abilities to potential employers.

Understand the AI Development Ecosystem

AI development these days relies on an advanced system of platforms, tools, and frameworks. Upcoming developers need to learn popular frameworks like TensorFlow, PyTorch, and scikit-learn. Clouds such as AWS, Google Cloud, and Azure enable infrastructure for AI development.

Understand this environment to excel better as artificial intelligence professionals and to work with teams. The trick is to start with a blank slate of necessary tools and build up knowledge gradually based on project requirements and professional goals. Practical knowledge with the tools, rather than theory, is more valuable.

Building Domain Knowledge

Effective creators of AI are usually technically proficient with advanced domain expertise. Understanding the specific needs and challenges of industries like medicine, finance, or consumer markets makes developers more effective and useful. Domain knowledge eases the production of the right solution and stakeholder interaction.

New AI developers may wish to specialize in a particular industry or area of interest. Specialization leads to more focused career development and better job opportunities. Reading industry publications, attending conferences, and working on domain projects builds this expertise.

Building a Professional Network

Networking is a critical career progression aspect in artificial intelligence. The AI world is also open to admitting new entrants as well, and most experienced developers are ready to help and impart advice. Professional networks can provide employment, collaboration, and learning avenues.

Potential artificial intelligence creators ought to visit online forums, local meetups, and social media content related to AI. Participating in open-source projects, blogging, and public speaking will make them popular and well-respected within the community.

Staying Current with Industry Trends

The field of AI keeps developing, so artificial intelligence developers are constantly in need of learning. Remaining ahead of the emerging trends and practices requires developers to remain updated by adhering to industry journals, research studies, and industry leaders. Such learning is useful for professional growth and solving problems.

New AI developers have to create learning habits that include reading research papers, keeping an eye on AI blogs, and testing new methods. One does not have to be up to date with everything but keep the ear to the ground on big breakthroughs and know which trends could impact work.

Preparation for the Job Search

Active job searching is needed to get employed in AI development. Early job titles may be "Machine Learning Engineer," "Data Scientist," or "AI Developer." Familiarity with the requirements of these positions is good preparation for applicants.

Successful AI programmers generally start in related careers and then move to AI programming over time. Research, software development, or data analysis would be a solid starting position with networking opportunities. Above all, one needs to be interested in AI programming and be willing to continually learn associated skills.

To become an hire artificial intelligence developers is not a marathon, it's a sprint. With its intricacy and ceaseless revolution, the learning is never over. New developers have to understand this reality and focus on establishing strong foundations and maintaining curiosity and flexibility.

The greatest AI developers are individuals who view obstacles as learning opportunities and are not deterred by unavoidable failure. With persistence, deliberate learning, and experience within the real world, new developers can build successful professional lives within this tech and high-impact field.

[xaltius]

How to Market Yourself as a Data Professional on LinkedIn?

In the dynamic and highly competitive world of data science, being good at your craft isn't enough. You need to be seen as good. And in 2025, there's no better platform for data professionals to build their personal brand, showcase expertise, and unearth opportunities than LinkedIn.

Think of LinkedIn not just as a job board, but as your professional portfolio, networking hub, and personal publishing platform rolled into one. Leveraging it strategically can open doors you never knew existed.

Here's how to market yourself as a data professional on LinkedIn like a pro:

1. Optimize Your Profile: Your Digital Shop Window

Your LinkedIn profile is your professional storefront. Make it shine!

Headline (Your AI-Powered Elevator Pitch): This is prime real estate. Don't just list your job title. Use keywords to clearly state your expertise and aspirations.

Instead of: "Data Scientist at XYZ Corp"

Try: "Senior Data Scientist | Machine Learning Engineer | NLP Specialist | Driving Business Impact with AI | Python, SQL, Cloud"

About Section (Your Narrative): Go beyond a dry summary. Craft a compelling story about your journey, passions, and the kind of impact you want to make. Highlight your key skills and areas of interest. Use keywords naturally throughout.

Experience (Quantify Your Impact): For each role, don't just list responsibilities. Focus on achievements and quantify them with metrics.

Instead of: "Developed machine learning models."

Try: "Developed and deployed predictive models for customer churn, resulting in a 15% reduction in churn rate and $X million in annualized savings."

Skills (The Algorithm's Friend): Be comprehensive. List relevant technical skills (Python, R, SQL, TensorFlow, PyTorch, AWS, Azure, GCP, Spark, Tableau, Power BI) and crucial soft skills (communication, problem-solving, collaboration, critical thinking, storytelling with data). Get endorsements from colleagues.

Education & Certifications: Showcase your academic background, specialized bootcamps, and industry certifications (e.g., AWS Certified Machine Learning Specialty, Google Cloud Professional Data Engineer).

Recommendations: Actively request recommendations from former managers, colleagues, or clients who can speak to your skills, work ethic, and impact. These are gold.

2. Showcase Your Work: Let Your Projects Speak

A data professional's portfolio is their strongest resume. LinkedIn's Project and Posts features are perfect for this.

Projects Section: This is where you link out to your work.

GitHub: Share links to well-documented code repositories.

Kaggle: Link your profile if you're active in competitions or sharing notebooks.

Personal Website/Blog: If you have one, link to case studies or interactive dashboards.

Interactive Dashboards: Share links to your Tableau Public, Power BI, or Streamlit apps that showcase your data visualization and storytelling skills.

Posts/Articles: Regularly share updates on your projects. Describe the problem, your approach, the tools you used, and the insights gained. Visuals (charts, screenshots) are highly encouraged.

3. Engage Strategically: Be Part of the Conversation

LinkedIn is a two-way street. Don't just broadcast; engage!

Follow Industry Influencers & Companies: Stay updated on trends, new technologies, and hiring announcements. Engage thoughtfully with their content.

Join Relevant Groups: Participate in data science, AI, ML, or industry-specific groups. Ask questions, offer insights, and share relevant resources.

Comment Thoughtfully: Don't just "like" posts. Add value by sharing your perspective, asking clarifying questions, or contributing additional information. This helps you get noticed.

Share Relevant Content: Curate insightful articles, research papers, industry news, or helpful tutorials. Position yourself as someone who stays informed and shares valuable knowledge.

4. Create Your Own Content: Establish Thought Leadership

This is where you move from being seen as a data professional to being seen as a leader in data.

LinkedIn Articles: Use this for longer-form content. Write detailed tutorials, share case studies of your projects, discuss industry trends, or offer career advice for aspiring data scientists.

Short Posts: Quick tips, observations, interesting findings from a dataset, or questions to spark discussion. Polls are great for engagement.

"Carousels" / Document Posts: Create visually appealing, multi-slide posts that summarize complex concepts, project steps, or key takeaways. These are highly shareable and engaging.

Video: Consider short videos explaining a concept or walking through a quick demo.

5. Network Proactively: Build Genuine Connections

LinkedIn is fundamentally about connections.

Personalized Connection Requests: Always, always, always add a personalized note. Explain why you want to connect (e.g., "Enjoyed your recent post on MLOps," "Saw your work at [company] and admire [project]").

Attend Virtual Events/Webinars: LinkedIn often hosts or promotes these. Engage with speakers and other attendees in the chat.

Informational Interviews: Reach out to experienced professionals in roles or companies that interest you. Request a brief virtual coffee chat to learn about their journey and advice (be respectful of their time and prepare specific questions).

Common Mistakes to Avoid

Generic Profile: A bare-bones profile tells recruiters nothing.

No Activity: A static profile suggests disinterest or lack of current engagement.

Only Applying for Jobs: If your only activity is applying for jobs, you miss out on building a reputation that attracts opportunities.

Poorly Articulated Achievements: Don't assume recruiters understand the technical jargon. Translate your impact into business value.

By consistently implementing these strategies, you'll transform your LinkedIn profile into a dynamic, compelling representation of your skills, expertise, and passion for data. It's not just about finding your next job; it's about building a sustainable personal brand that positions you as a valuable asset in the ever-evolving data landscape. Start marketing yourself today!

[nuveprotechnologies]

Skills Ontology and New Skills: How to Continuously Update Your Skill Set

In the dynamic and ever-evolving world of technology, standing still is equivalent to falling behind. With constant innovation, the skillsets that were valuable yesterday may not be enough to meet today’s challenges — let alone tomorrow’s. This is where the concept of skills ontology becomes vital. Skills ontology refers to the structured mapping of skills and their relationships, helping learners and organizations identify the skills needed for specific roles, how they evolve, and how they relate to other competencies. 

The future of workforce development lies in recognizing skill clusters, identifying emerging needs, and continuously updating competencies through immersive, practical, and targeted learning experiences. And Nuvepro, a leader in workforce skilling solutions, is redefining how individuals and enterprises can navigate this journey. 

Why Skills Ontology Matters 

In a digital-first economy, IT professionals are no longer defined by a single expertise. Modern roles require a blend of technologies and soft skills — a front-end developer may also need a grasp of UX principles, cloud deployment, and CI/CD tools. Understanding these interrelated skills is made possible through a skills ontology, which serves as a dynamic blueprint to design relevant learning paths and predict future needs. 

But identifying skills is only one part of the equation. The real challenge lies in continuously updating and validating those skills to ensure job readiness and project readine 

Continuous Learning Through Hands-On Practice 

Traditional learning models — long courses, video tutorials, or theoretical lectures — often fail to create real transformation. Today’s IT professionals need learning by doing, and that’s where Nuvepro excels. Through its hands-on sandboxes and sandbox environments, Nuvepro provides learners the opportunity to experiment, fail, learn, and succeed — all within a controlled, real-world-like environment. 

These sandboxes for skilling and challenge labs allow learners to work on live-like projects, gaining the confidence and competence that textbook learning can’t offer. Whether it's deploying Docker containers, setting up a CI/CD pipeline, or integrating Azure with ASP.NET Core applications, Nuvepro ensures learners acquire skills the way they’re actually used in projects. 

Adapting to New Skill Requirements in Real-Time 

Technology doesn’t wait — and neither should learning. With Nuvepro’s workforce training programs, companies can introduce emerging skills into their skilling pipelines instantly. Whether it's a new cloud platform, an updated security framework, or evolving AI development tools, Nuvepro helps businesses and learners stay on top of change. 

By integrating on-the-job training modules and job training programs, IT companies can ensure their workforce remains competitive and current. This agility is crucial not just for innovation, but also for reducing bench time and accelerating time-to-billability for new hires and cross-skilled professionals. 

From Awareness to Application: The Skilling Journey 

Updating your skill set begins with identifying gaps and ends with validation. Nuvepro’s platform allows for both. With a structured learning flow that combines hands-on learning with post-training assessments, learners don’t just gain new skills — they prove them. This comprehensive approach turns training into tangible outcomes, not just completed checklists. 

By aligning skills ontology with Nuvepro’s skilling solutions, organizations gain a clear roadmap from skill gaps to deployable talent. The result? A workforce that is more resilient, more confident, and more future-proof. 

Future-Proofing IT Teams with Nuvepro 

The speed at which technology evolves will only increase. Organizations that don’t invest in continuous skilling will find themselves struggling to compete. Nuvepro's sandbox-based training approach provides a strategic, scalable, and deeply practical way to close skill gaps, prepare for emerging technologies, and ensure team readiness for real-world challenges. 

From training for IT companies to project readiness initiatives, Nuvepro is not just building individual competencies — it's enabling organisations to build the workforce of the future. 

Conclusion:  

In a world driven by skills and defined by speed, continuous learning is no longer optional — it’s the foundation of professional survival and success. With a deep understanding of skills ontology, paired with Nuvepro’s robust hands-on solutions, IT professionals can embrace change, master new technologies, and stay consistently job-ready. 

Whether you’re an individual aiming to upskill or an organization seeking to future-proof your talent, Nuvepro offers the tools, environments, and strategies to help you succeed — not just today, but for what comes next.

[ameliasoulturner]

Your 2025 Roadmap to Becoming an AI/ML Pro and Landing That Dream Job

If you're reading this, chances are you’ve heard the buzz: artificial intelligence and machine learning are changing the world. From self-driving cars to ChatGPT writing college essays (I’m not saying I condone it), AI is no longer a futuristic dream—it’s right here, right now. And if you’re thinking about jumping into the field, let me tell you something important: there’s never been a better time to start than 2025.

But where do you begin? How do you go from not knowing what “gradient descent” even means to scoring a real, paying job in AI or ML? Whether you're a college student, a career switcher, or just plain curious, this article will lay out your step-by-step roadmap to mastering AI/ML and getting hired. No fluff. No jargon overload. Just the good stuff.

Let’s break it down.

Step 1: Start with the “Why”

Before we dive into Python, TensorFlow, or data lakes, take a step back and ask yourself: why AI or machine learning?

Are you fascinated by how Netflix knows what you want to watch before you do? Do you want to build the next breakthrough medical diagnostic system? Or maybe you just want a job that pays six figures and lets you work in sweatpants. All are valid.

Knowing your "why" helps you stay motivated when the math gets messy or your model accuracy tanks at 47%. Spoiler: it will, and that’s okay.

Step 2: Learn the Basics (No, You Don’t Need a PhD)

Let’s bust a myth right now: you do not need a PhD to get into AI or ML. Sure, some roles require deep research experience, but most jobs in the real world need problem solvers, not paper publishers.

Here’s what you need to get started:

Python: The unofficial language of AI. Easy to learn, powerful to use.

Math fundamentals: Linear algebra, probability, statistics, and calculus. You don’t need to master them all at once—just understand enough to know what’s happening behind the scenes.

Data handling: Learn to clean, analyze, and visualize data using libraries like Pandas and Matplotlib.

Basic machine learning: Get hands-on with Scikit-learn. Train a model. Test it. Rinse and repeat.

Platforms like Coursera, edX, Udemy, and freeCodeCamp offer beginner-friendly courses. Andrew Ng’s ML course on Coursera is still the gold standard—and guess what? It’s free.

Step 3: Dive into Real Projects (Even If You Don’t Know What You’re Doing Yet)

Theory’s great, but nothing beats learning by doing. Start building small projects. Don’t wait until you “know enough”—you’ll learn more by making mistakes than by watching tutorials all day.

Here are some beginner-to-intermediate project ideas:

Predict housing prices using regression models.

Classify images of cats vs. dogs.

Build a spam filter using natural language processing.

Create a recommendation system for books or movies.

Push your code to GitHub. Write about your projects on Medium or LinkedIn. This not only reinforces what you learn but also shows potential employers that you’re serious.

Step 4: Learn Deep Learning and AI Frameworks

Once you’ve got the basics down, it’s time to step into the world of neural networks.

Focus on:

Deep learning basics: Understand what neural networks are and how they work.

Keras and TensorFlow: Great for beginners. PyTorch is equally popular and used heavily in research.

CNNs and RNNs: Used for image and sequence data respectively.

Transformers and LLMs: These power tools like ChatGPT and are shaping the future of AI.

There are fantastic free courses like the Deep Learning Specialization by Andrew Ng and Fast.ai’s deep learning course that walks you through building real models, fast.

Step 5: Get Familiar with the Tools of the Trade

Just like carpenters have their hammers and saws, AI pros have their own toolkit. These are the tools hiring managers expect you to know:

Jupyter Notebooks: Perfect for data exploration and experimentation.

Git and GitHub: Version control and portfolio showcase.

Cloud platforms: AWS, GCP, and Azure offer free tiers where you can train models.

Docker and APIs: For deploying and sharing your work.

Don’t stress about mastering them all on day one. Add them gradually to your workflow as your projects grow.

Step 6: Build a Killer Portfolio

Here’s the secret: a great portfolio beats a fancy degree.

What should you include?

3-5 polished projects that show a range of skills—classification, NLP, image processing, recommendation systems.

Problem-solving focus: Employers love real-world impact. Try to solve problems in industries you care about—healthcare, finance, e-commerce, etc.

Write about your work: Blog posts, walkthrough videos, GitHub READMEs. Make it easy for recruiters to see how you think.

If you can show that you understand the business problem, chose the right model, and communicated the results clearly, you’re golden.

Step 7: Get Involved in the Community

AI and ML are fast-moving fields. What’s hot today could be old news tomorrow. One of the best ways to stay updated—and grow—is to engage with the community.

Here’s how:

Follow AI influencers on X (formerly Twitter), LinkedIn, and YouTube

Join AI subreddits like r/MachineLearning and r/learnmachinelearning

Attend virtual meetups, hackathons, and AI conferences

Contribute to open-source projects

You'll learn faster, meet people who can refer you to jobs, and maybe even land a mentor or two.

Step 8: Tailor Your Resume and LinkedIn for AI Jobs

You’ve got the skills. You’ve got the projects. Now let’s package it right.

Resume tips:

Use keywords from job descriptions (e.g., “machine learning”, “NLP”, “model deployment”)

Highlight project outcomes, not just tech stacks

Mention relevant certifications and courses

LinkedIn tips:

Write a short, compelling summary that explains what kind of problems you solve with AI

Feature your projects and GitHub repo

Engage with AI content and connect with recruiters

Step 9: Apply Strategically—and Don’t Wait to Feel “Ready”

Imposter syndrome is real. But here’s the deal: you won’t ever feel 100% ready—and that’s normal.

Start applying to entry-level roles, internships, apprenticeships, and remote freelance gigs. Use platforms like:

LinkedIn Jobs

AngelList (for startups)

Wellfound

AI-focused job boards like ai-jobs.net

Customize your resume and cover letter for each job. And keep a spreadsheet to track your applications and follow-ups.

Step 10: Keep Leveling Up After You Get the Job

Once you land a role, the learning doesn’t stop.

AI is evolving rapidly in 2025. New models, new frameworks, new ethical concerns—it’s a field in motion.

Stay sharp by:

Subscribing to newsletters like The Batch or Import AI

Reading new AI research papers (try arXiv-sanity)

Taking specialized courses in areas like reinforcement learning, AI ethics, and generative models

Bonus tip: If you’re aiming for big tech, learn system design and start practicing AI interviews. Sites like LeetCode and Interviewing.io are gold.

Wrapping It All Up

So, can you go from AI newbie to pro in 2025? Absolutely. But it takes focus, curiosity, and the willingness to get your hands dirty.

The path isn’t a straight line. You’ll hit walls. You’ll debug weird errors at 2 AM. But if you stick with it, build real things, and stay connected to the community, you’ll be amazed how far you can go in a year.

[praveennareshit]

Where to learn cloud computing for free ?

💡 Where to Learn Cloud Computing for Free? Your First Steps in 2025

So you’ve heard about cloud computing. Maybe you’ve seen the job titles—Cloud Engineer, DevOps Specialist, AWS Associate—or heard stories of people getting hired after just a few months of learning.

Naturally, you wonder: "Can I learn cloud computing for free?"

Yes, you can. But you need to know where to look, and what free learning can (and can’t) do for your career.

☁️ What Can You Learn for Free?

Free cloud resources help you understand:

What cloud computing is

How AWS, Azure, or GCP platforms work

Basic terminology: IaaS, SaaS, EC2, S3, VPC, IAM

Free-tier services (you can try cloud tools without being charged)

Entry-level certification concepts

🌐 Best Places to Learn Cloud for Free (Legit Sources)

✅ AWS Free Training & AWS Skill Builder

📍 https://aws.amazon.com/training

Learn cloud basics with self-paced videos

Good for AWS Cloud Practitioner prep

Access real AWS Console via the free tier

✅ Microsoft Learn – Azure

📍 https://learn.microsoft.com/en-us/training

Offers free Azure paths for beginners

Gamified learning experience

Ideal for preparing for AZ-900

✅ Google Cloud Skills Boost

📍 https://cloud.google.com/training

Interactive quests and labs

Learn Compute Engine, IAM, BigQuery

Some credits may be needed for advanced labs

✅ YouTube Channels

FreeCodeCamp: Full cloud crash courses

Simplilearn, Edureka, NareshIT: Basic tutorials

AWS, Azure official channels: Real demos

✅ GitHub Repos & Blogs

Open-source lab guides

Resume projects (e.g., deploy a website on AWS)

Real-world practice material

⚠️ But Wait—Here’s What Free Learning Misses

While free content is great to start, most learners eventually hit a wall:

❌ No structured syllabus

❌ No mentor to answer questions

❌ No feedback on real projects

❌ No resume guidance or placement support

❌ Certification confusion (what to take, when, why?)

That’s where formal, hands-on training can make the difference—especially if you want to get hired.

🎓 Want to Learn Faster, Smarter? Try NareshIT’s Cloud Courses

At NareshIT, we’ve helped over 100,000 learners start their cloud journey—with or without a tech background.

We bridge the gap between free concepts and job-ready skills.

🔹 AWS Cloud Beginner Program

Duration: 60 Days

Covers EC2, IAM, S3, Lambda

Includes: AWS Cloud Practitioner & Associate exam prep

Ideal For: Freshers, support engineers, and non-coders

🔹 Azure Fundamentals Course

Duration: 45 Days

Learn VMs, Azure AD, Blob Storage, and DevOps basics

Prepares you for AZ-900 and AZ-104 certifications

Best For: IT admins and .NET developers

🔹 Google Cloud (GCP) Basics

Duration: 30 Days

Practice labs + GCP Associate Cloud Engineer training

Perfect for: Python devs, data learners, AI enthusiasts

📅 View all cloud training batches at NareshIT

👣 Final Words: Start Free. Scale Smart.

If you’re serious about cloud, there’s no shame in starting with free videos or cloud tutorials. That’s how many great engineers begin.

But when you’re ready to:

Work on real projects

Earn certifications

Prepare for interviews

Get career guidance

Then it’s time to consider a guided course like the ones at NareshIT.

📌 Explore new batches →

[tpointtechedu]

Data Science Tutorial for 2025: Tools, Trends, and Techniques

Data science continues to be one of the most dynamic and high-impact fields in technology, with new tools and methodologies evolving rapidly. As we enter 2025, data science is more than just crunching numbers—it's about building intelligent systems, automating decision-making, and unlocking insights from complex data at scale.

Whether you're a beginner or a working professional looking to sharpen your skills, this tutorial will guide you through the essential tools, the latest trends, and the most effective techniques shaping data science in 2025.

What is Data Science?

At its core, data science is the interdisciplinary field that combines statistics, computer science, and domain expertise to extract meaningful insights from structured and unstructured data. It involves collecting data, cleaning and processing it, analyzing patterns, and building predictive or explanatory models.

Data scientists are problem-solvers, storytellers, and innovators. Their work influences business strategies, public policy, healthcare solutions, and even climate models.

Essential Tools for Data Science in 2025

The data science toolkit has matured significantly, with tools becoming more powerful, user-friendly, and integrated with AI. Here are the must-know tools for 2025:

1. Python 3.12+

Python remains the most widely used language in data science due to its simplicity and vast ecosystem. In 2025, the latest Python versions offer faster performance and better support for concurrency—making large-scale data operations smoother.

Popular Libraries:

Pandas: For data manipulation

NumPy: For numerical computing

Matplotlib / Seaborn / Plotly: For data visualization

Scikit-learn: For traditional machine learning

XGBoost / LightGBM: For gradient boosting models

2. JupyterLab

The evolution of the classic Jupyter Notebook, JupyterLab, is now the default environment for exploratory data analysis, allowing a modular, tabbed interface with support for terminals, text editors, and rich output.

3. Apache Spark with PySpark

Handling massive datasets? PySpark—Python’s interface to Apache Spark—is ideal for distributed data processing across clusters, now deeply integrated with cloud platforms like Databricks and Snowflake.

4. Cloud Platforms (AWS, Azure, Google Cloud)

In 2025, most data science workloads run on the cloud. Services like Amazon SageMaker, Azure Machine Learning, and Google Vertex AI simplify model training, deployment, and monitoring.

5. AutoML & No-Code Tools

Tools like DataRobot, Google AutoML, and H2O.ai now offer drag-and-drop model building and optimization. These are powerful for non-coders and help accelerate workflows for pros.

Top Data Science Trends in 2025

1. Generative AI for Data Science

With the rise of large language models (LLMs), generative AI now assists data scientists in code generation, data exploration, and feature engineering. Tools like OpenAI's ChatGPT for Code and GitHub Copilot help automate repetitive tasks.

2. Data-Centric AI

Rather than obsessing over model architecture, 2025’s best practices focus on improving the quality of data—through labeling, augmentation, and domain understanding. Clean data beats complex models.

3. MLOps Maturity

MLOps—machine learning operations—is no longer optional. In 2025, companies treat ML models like software, with versioning, monitoring, CI/CD pipelines, and reproducibility built-in from the start.

4. Explainable AI (XAI)

As AI impacts sensitive areas like finance and healthcare, transparency is crucial. Tools like SHAP, LIME, and InterpretML help data scientists explain model predictions to stakeholders and regulators.

5. Edge Data Science

With IoT devices and on-device AI becoming the norm, edge computing allows models to run in real-time on smartphones, sensors, and drones—opening new use cases from agriculture to autonomous vehicles.

Core Techniques Every Data Scientist Should Know in 2025

Whether you’re starting out or upskilling, mastering these foundational techniques is critical:

1. Data Wrangling

Before any analysis begins, data must be cleaned and reshaped. Techniques include:

Handling missing values

Normalization and standardization

Encoding categorical variables

Time series transformation

2. Exploratory Data Analysis (EDA)

EDA is about understanding your dataset through visualization and summary statistics. Use histograms, scatter plots, correlation heatmaps, and boxplots to uncover trends and outliers.

3. Machine Learning Basics

Classification (e.g., predicting if a customer will churn)

Regression (e.g., predicting house prices)

Clustering (e.g., customer segmentation)

Dimensionality Reduction (e.g., PCA, t-SNE for visualization)

4. Deep Learning (Optional but Useful)

If you're working with images, text, or audio, deep learning with TensorFlow, PyTorch, or Keras can be invaluable. Hugging Face’s transformers make it easier than ever to work with large models.

5. Model Evaluation

Learn how to assess model performance with:

Accuracy, Precision, Recall, F1 Score

ROC-AUC Curve

Cross-validation

Confusion Matrix

Final Thoughts

As we move deeper into 2025, data science tutorial continues to be an exciting blend of math, coding, and real-world impact. Whether you're analyzing customer behavior, improving healthcare diagnostics, or predicting financial markets, your toolkit and mindset will be your most valuable assets.

Start by learning the fundamentals, keep experimenting with new tools, and stay updated with emerging trends. The best data scientists aren’t just great with code—they’re lifelong learners who turn data into decisions.

[dhruvdave4446]

How Mobile App Development Services Drive User Engagement and Retention

1. Introduction

In today’s competitive digital landscape, mobile apps are no longer a luxury—they’re a necessity. As of 2025, global smartphone users surpass 7 billion, and over 90% of mobile time is spent on apps. But launching an app isn't enough. Without sustained user engagement and high retention, even the most innovative apps can fade into obscurity.

This is where Mobile App Development Services play a pivotal role. From intuitive UI/UX design to personalized user journeys and real-time analytics, professional app development goes beyond coding—it’s about creating lasting connections between brands and users.

2. Understanding User Engagement and Retention

Before diving into strategies, it’s vital to define the two pillars of mobile app success:

User Engagement refers to how actively users interact with the app—measured by metrics like session length, frequency of use, and feature interaction.

User Retention is the ability of the app to keep users returning over time, typically measured at intervals like Day 1, Day 7, and Day 30 retention rates.

Churn rate (users who stop using the app) is the inverse of retention, and minimizing it is key for long-term app performance.

3. Core Features of Mobile App Development Services That Boost Engagement

Professional Mobile App Development Services are designed with engagement in mind, integrating technical and experiential features:

✅ Personalization

AI and machine learning personalize content based on user behavior. Netflix-style recommendations or eCommerce product suggestions improve relevance and keep users interested.

✅ Push Notifications

Modern services implement smart push notifications—those that adapt based on user behavior, location, and preferences. Well-timed, relevant alerts significantly increase re-engagement.

✅ Onboarding Experiences

First impressions matter. Development teams ensure onboarding is smooth, fast, and informative. Interactive tutorials and minimal friction steps reduce drop-offs.

✅ UI/UX Design

An intuitive design is a cornerstone of engagement. Clean navigation, accessible layout, and gesture-friendly interactions make users stick around longer.

4. Retention Strategies Enabled by Mobile App Development Services

Mobile App Development Services also help in building retention mechanisms directly into the app’s architecture.

🔁 In-App Messaging and Loyalty Programs

Real-time chat, customer support, and loyalty rewards foster a sense of community. Points, vouchers, or tiered rewards encourage repeat usage.

🎮 Gamification

Turning everyday interactions into achievements (e.g., streaks, badges) keeps users coming back. Apps like Duolingo have mastered this.

📶 Offline Functionality

Especially in areas with low connectivity, offline features ensure usability anytime. This not only boosts retention but expands your user base.

🔄 Cross-Platform Sync

Apps that sync across multiple devices (e.g., mobile, tablet, wearables) make usage seamless, allowing users to pick up where they left off.

5. Technical Innovations That Enhance Engagement

Behind the scenes, several advanced technologies power engagement strategies:

📊 Analytics Integration

Modern development services integrate tools like Firebase, Mixpanel, or Amplitude to monitor user behavior. Real-time data drives UX improvements and targeted updates.

🤖 Machine Learning for Prediction

ML models can identify churn-prone users and trigger automated retention campaigns (e.g., special offers or personalized nudges).

🔗 IoT and Wearable Integration

Especially relevant in health, fitness, and smart home apps, integrations with devices like smartwatches and trackers increase interaction points.

☁️ Cloud Infrastructure

Faster performance and global content delivery are enabled through cloud solutions like AWS, Azure, or Google Cloud. Speed is critical to retention—every second delay increases bounce risk.

6. Case Studies / Industry Examples

🏋️ Fitness App Example

A fitness app integrated personalized workout routines using user data and wearables. Engagement rates rose by 40%, while 30-day retention improved by 25% after gamification was introduced.

🛍️ E-Commerce App Example

Using dynamic push notifications for abandoned carts and flash sales, an e-commerce app boosted return visits by 50%. Tailored Mobile App Development Services enabled A/B testing for continuous improvement.

7. Choosing the Right Mobile App Development Partner

Not all app developers are equal. When selecting a Mobile App Development Services provider, prioritize:

Proven experience across platforms (iOS, Android, cross-platform)

Expertise in UI/UX, backend architecture, and data security

Support for integration of advanced features like AI, cloud, and analytics

Post-launch support for updates, user feedback loops, and scaling

Ongoing maintenance and evolution of the app are just as crucial as the initial build.

8. Conclusion

In 2025’s saturated app market, user attention is hard-won and easily lost. Engagement and retention are no longer optional—they’re essential KPIs that define success. With the right Mobile App Development Services, businesses can design feature-rich, technically sound, and highly engaging mobile apps that users not only download—but keep using.

[divya4567]

Best Programming Languages for Automation Testing Beginners

Stepping into the world of automation testing can feel overwhelming at first—especially when it comes to choosing the right programming language. The good news? You don’t need to be a software developer to learn automation testing. What you do need is a solid grasp of one programming language that aligns well with testing tools and frameworks.

If you're planning to enroll in Automation Testing Classes in Pune, you’ll likely start by learning one of these beginner-friendly languages. Let’s explore the best options to begin your journey in test automation.

1. Java – The Most Popular Choice

Java is one of the most widely used languages in automation testing, especially with Selenium WebDriver. Its strong community support, abundance of learning resources, and wide adoption by companies make it a top choice for beginners.

Why Java?

Seamless integration with Selenium, TestNG, Appium, and other tools

Strong object-oriented structure, which helps in building reusable frameworks

Tons of tutorials and documentation available for self-study

If you're attending structured Automation Testing Classes in Pune, chances are you'll be introduced to Java early in the course.

2. Python – Simple and Readable

Python is becoming increasingly popular among new testers due to its simple syntax and clean code structure. It’s beginner-friendly and versatile, making it ideal for those who are intimidated by traditional programming languages.

Why Python?

Shorter learning curve for non-coders

Compatible with testing tools like PyTest, Selenium, and Robot Framework

Growing use in API and AI-based testing

Python is an excellent starting point if you're looking to transition from manual to automation testing without getting bogged down by complex code.

3. JavaScript – For Web-Focused Testers

If your focus is web testing or front-end automation, JavaScript is a strong contender. Modern tools like Cypress and Playwright use JavaScript or TypeScript and offer powerful features for end-to-end testing.

Why JavaScript?

Great for full-stack testers or those working in JavaScript-heavy environments

Tools like Cypress and Playwright are quick, modern, and developer-friendly

Ideal for testers who work closely with frontend development teams

Enrolling in Automation Testing Classes in Pune that include modern web automation tools will often expose you to JavaScript-based frameworks.

4. C# – A Strong Option for .NET Environments

C# is widely used in organizations that rely on Microsoft technologies. Paired with Selenium and NUnit, it provides robust support for automation in Windows-based systems.

Why C#?

Well-suited for testers working in .NET development environments

Clean syntax and strong performance

Easy integration with Visual Studio and Azure DevOps

While not as commonly taught in beginner courses as Java or Python, C# is worth considering if you're targeting .NET companies.

Final Thoughts: Choose One and Go Deep

It’s easy to get distracted by all the options, but remember: you don’t need to learn every language. Start with one that aligns with your course or career goals and go deep. Java and Python are often recommended for beginners due to their simplicity, popularity, and wide tool compatibility.

If you're still unsure, joining a structured program like the Automation Testing Classes in Pune offered by trusted institutes can guide your choice based on current industry demand and job market trends.

About Skillio

Skillio (formerly Testing Shastra) is Pune’s trusted name in software testing education. Known for its job-focused curriculum, Skillio trains students in top automation tools and programming languages, helping them build real-world skills from day one. Whether you’re starting fresh or upskilling, Skillio’s expert-led Automation Testing Classes in Pune are designed to get you job-ready fast.

To get more information about such IT courses from Skillio,

Contact details —

Website: https://helloskillio.com/

Address: 504, Ganeesham E, Pimple Saudagar, Pune.

Email: info@helloskillio.com

Get directions

[pallaviicert]

Microsoft Azure Tutorial for Beginners

With today's digitally fueled times, cloud computing is the linchpin of modern business practices. One of the leading cloud platforms, Microsoft Azure offers an all-encompassing portfolio of services and solutions to developers, IT specialists, and businesses. Whether hosting applications, safely storing data, or utilizing artificial intelligence, Azure does it all. If you are just starting with cloud computing or just wish to learn all about Azure from scratch, then this tutorial is for you. Let's navigate step by step through the fundamentals and introduce Microsoft Azure to beginners. What is Microsoft Azure? Microsoft Azure is a cloud computing platform and Microsoft's cloud computing platform. It provides Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) to assist customers to create, deploy, and manage applications in Microsoft data centers.

From web hosting to creating intelligent applications with AI, Azure serves more than 200 products and cloud services that touch computing, networking, storage, databases, analytics, and more. Why Choose Azure? There are a number of reasons why Azure is a top choice for novices and experts alike: • Worldwide Reach: Azure has more data centers than any other vendor in more locations. • Hybrid Capabilities: Smooth integration between on-premise and cloud environments. • Security: Integrated security controls, compliance software, and multi-layered security. • Scalability: Simply scale up or down as needed. • Integration with Microsoft Stack: Native integration with offerings such as Windows Server, SQL Server, Office 365, and many more.

Getting Started with Azure 1. Create a Microsoft Azure Account To begin using Azure, you’ll need to create a free account. Microsoft offers a 30-day trial with $200 credit and 12 months of free services for beginners. • Visit https://azure.microsoft.com • Click on "Start Free" • Sign in with your Microsoft account or create one Once your account is created, you’ll gain access to the Azure Portal, your dashboard for managing and monitoring resources. 2. Exploring the Azure Portal Azure Portal is an on-line portal from which you are able to: • Create and setup cloud resources (VMs, databases, etc.) • Watch for activity and utilization • Setup budget and warnings • View bills and billing • Run cloud shell to script Any action that can be taken on Azure, whether it be deploying virtual machines or setting up resource groups, is possible via this portal.

Learning Azure Core Concepts 1. Subscriptions Your subscription is your contract with Microsoft to utilize Azure services. Your subscription establishes the billing scope and associates your usage with an account. 2. Resource Groups A resource group is a grouping that contains associated resources like databases, virtual machines, and storage accounts. It makes it easy for you to manage them in one place. 3. Regions and Availability Zones Azure resources are run in regions, which are actual locations on Earth. Every region has availability zones-independent datacenters to provide fault tolerance and high availability. 4. Azure Resource Manager (ARM) ARM is the management and deployment service of Azure. ARM offers a predictable mechanism to create, update, and delete resources with templates or automation scripts.

Key Azure Services for Newbies Following are some of the key Azure services that you will be most likely using in your very first project: 1. Azure Virtual Machines (VMs) Azure VMs enable you to put a Windows or Linux server on the cloud. Use them for dev, test, or production-running applications. • Choose your OS • Choose your size (CPU, RAM) • Configure storage and networking • Deploy within minutes 2. Azure App Services App Services allow you to host web apps and RESTful APIs into a fully managed platform. No infrastructure to deal with. • Inherent scaling • .NET, Node.js, Java, Python, PHP support • CI/CD built-in 3. Azure Blob Storage A highly scalable object store for unstructured data such as images, videos, documents, and backups. • Accessible over REST APIs • Cost-effective storage tiers • Lifecycle management policies 4. Azure SQL Database A very managed relational database as a service on Microsoft SQL Server. Ideal for SQL beginners. • Automated backups and patching • In-built performance tuning with native intelligence • Elastic scale 5. Azure Functions A serverless computing platform that runs your code on demand in response to event-driven triggers such as HTTP requests, events, or timers. Ideal for: • Workloads automation • Real-time file processing • Light web APIs

Managing and Monitoring Resources Azure Monitor Tracks the performance of services and applications. Use it to: • Schedule alerts • View dashboards • Analyze logs Azure Cost Management + Billing Enables you to monitor cloud spending and effectively manage budgets. • Real-time cost analysis • Budget notifications • Cost-saving recommendations Azure Advisor Gives you customized best practices for: • Performance • Security • High availability • Cost optimization

Azure Key Vault Securely store and manage secrets, encryption keys, and certificates.

Azure Deployment Mechanisms There are multiple deployment tools and interfaces supported by Azure: • Azure Portal: Graphical • Azure CLI: Scripting command-line • PowerShell: Excellent automation support • ARM Templates: Declarative JSON templates • Terraform: Third-party popular infrastructure-as-code

Azure Tips for Beginners 1. Begin with Free Tier: Get used to the free services like App Service, Blob Storage, and Azure SQL. 2. Use Tags: Add metadata to group and track resources. 3. Use Microsoft Learn: Interactive task-specific feature-specific tutorials. 4. Leverage Azure Sandbox: Sandbox space for free, secure experimentation. 5. Track Usage: Alert so as to avoid surprise bills.

Security and Identity in Azure Security is among the key areas of cloud computing. Azure provides a number of services to enable you to protect your data and control user access. Azure Active Directory (Azure AD) An identity and access management cloud service. Use it to: • Administrate users and groups • Implement Single Sign-On (SSO) • Implement access controls on apps Role-Based Access Control (RBAC) RBAC has high-granularity access control. Use roles to provide access to users so they see only what they need.

Avoid Common Mistakes • Cost management oversight avoidance: Continuously keep expenditures and usage in check. • Blind use of default settings: Adjust to performance and security instead. • Failing to preserve data: Use in-built facilities for backup purposes. • Opening too much up: Apply best practices for least privilege. • Monitoring failure: Set up dashboards and alerts in advance. Conclusion Microsoft Azure provides a solid, nimble platform for cloud hosting and development. Being an early user, beginning with basics, tools, and services is a suitable starting point to establish a learning foundation for further learning and hands-on exercises. Whether your target is to create applications, host infrastructure, or move to a cloud-specific role, Azure provides everything. Get your hands-on experience by experimenting with Azure's free services, guided learning paths, and applying them to real-world projects. The future lies in the cloud, and Microsoft Azure is the key.

Website: https://www.icertglobal.com/course/developing-microsoft-azure-solutions-70-532-certification-training/Classroom/80/3395

[hiringiosdevelopers]

Joining AI: Guide for Upcoming Artificial Intelligence Developers

The Booming Demand for AI Professionals

The artificial intelligence industry has expanded at breakneck speeds over the past few years, presenting new developers with more opportunities than ever. Transitions into careers as AI developers can seem daunting in the presence of the enormity and pace of industry changes. Familiarity with the landscape as well as a thoughtfully planned approach can ease the transition for new artificial intelligence developer  into a fulfilling yet demanding profession.

Creating Strong Mathematical Foundations

Mathematics is the backbone to building artificial intelligence. Future artificial intelligence developers would be well advised to possess extensive working knowledge of statistics, linear algebra, and calculus. Mathematical concepts are not pedantic principles,these are working tools developers use day in and day out to comprehend algorithms, tune models, and solve complex problems.

The good news is that math is learned in stages. New AI developers need not know each piece of math before starting, yet must be ready to learn on a daily basis. Online tutorials, books, and project learning are excellent vehicles for locking down math concepts as well as learning by doing.

Choosing the Right Programming Languages

Programming skill is necessary for any artificial intelligence developer, but the proper selection of languages will accelerate career growth. Python is currently the most popular language in AI development due to its vast collection of libraries and simplicity of use. R is needed for statistical data and data science, whereas Java and C++ are essential in production environments.

New AI developers will start with Python and later develop language proficiency over time based on career goals. A basic understanding of at least one language and experience with the relative advantages and applications of others is the greatest concern. There is more doing than talking about things that should be done, so project work is essential to programming skill acquisition.

Acquiring Pragmatic Experience Through Projects

Practical experience is in great demand among the upcoming generation of artificial intelligence coders. Side projects demonstrate skill to potential employers and permit learning new tools and techniques. Starting with simple projects and progressing to more complex ones develops confidence and skill.

Good starting projects would include image classification software, sentiment analysis software, or recommendation software. The plan is to choose projects that fit career goals but also cover various aspects of AI development. Documentation of projects and publishing on platforms like GitHub is a way of showcasing abilities to potential employers.

Understand the AI Development Ecosystem

AI development these days relies on an advanced system of platforms, tools, and frameworks. Upcoming developers need to learn popular frameworks like TensorFlow, PyTorch, and scikit-learn. Clouds such as AWS, Google Cloud, and Azure enable infrastructure for AI development.

Understand this environment to excel better as artificial intelligence professionals and to work with teams. The trick is to start with a blank slate of necessary tools and build up knowledge gradually based on project requirements and professional goals. Practical knowledge with the tools, rather than theory, is more valuable.

Building Domain Knowledge

Effective creators of AI are usually technically proficient with advanced domain expertise. Understanding the specific needs and challenges of industries like medicine, finance, or consumer markets makes developers more effective and useful. Domain knowledge eases the production of the right solution and stakeholder interaction.

New AI developers may wish to specialize in a particular industry or area of interest. Specialization leads to more focused career development and better job opportunities. Reading industry publications, attending conferences, and working on domain projects builds this expertise.

Building a Professional Network

Networking is a critical career progression aspect in artificial intelligence. The AI world is also open to admitting new entrants as well, and most experienced developers are ready to help and impart advice. Professional networks can provide employment, collaboration, and learning avenues.

Potential artificial intelligence creators ought to visit online forums, local meetups, and social media content related to AI. Participating in open-source projects, blogging, and public speaking will make them popular and well-respected within the community.

Staying Current with Industry Trends

The field of AI keeps developing, so artificial intelligence developers are constantly in need of learning. Remaining ahead of the emerging trends and practices requires developers to remain updated by adhering to industry journals, research studies, and industry leaders. Such learning is useful for professional growth and solving problems.

New AI developers have to create learning habits that include reading research papers, keeping an eye on AI blogs, and testing new methods. One does not have to be up to date with everything but keep the ear to the ground on big breakthroughs and know which trends could impact work.

Preparation for the Job Search

Active job searching is needed to get employed in AI development. Early job titles may be "Machine Learning Engineer," "Data Scientist," or "AI Developer." Familiarity with the requirements of these positions is good preparation for applicants.

Successful AI programmers generally start in related careers and then move to AI programming over time. Research, software development, or data analysis would be a solid starting position with networking opportunities. Above all, one needs to be interested in AI programming and be willing to continually learn associated skills.

To become an artificial intelligence developer is not a marathon, it's a sprint. With its intricacy and ceaseless revolution, the learning is never over. New developers have to understand this reality and focus on establishing strong foundations and maintaining curiosity and flexibility.

The greatest AI developers are individuals who view obstacles as learning opportunities and are not deterred by unavoidable failure. With persistence, deliberate learning, and experience within the real world, new developers can build successful professional lives within this tech and high-impact field.

[easyshiksha-article]

Getting Started with Microsoft Azure Machine Learning Studio: A Beginner’s Guide

In today’s digital-first world, the power of data science has moved from the hands of experts to the desktops of aspiring professionals, thanks to tools like Microsoft Azure Machine Learning Studio. Whether you're a student, a budding data scientist, or a professional looking to expand your skills, Azure ML Studio provides a simplified, drag-and-drop interface to build, train, and deploy machine learning models with minimal coding.

What Is Microsoft Azure Machine Learning Studio?

Microsoft Azure Machine Learning Studio is a web-based, integrated development environment (IDE) built for data science and machine learning workflows. It allows users to create predictive models using pre-built modules and workflows without needing to write extensive code.

Unlike traditional machine learning platforms that often require deep programming knowledge, Azure ML Studio is designed for accessibility. Users can simply drag datasets, apply algorithms, and connect models—all from a visual interface.

Key Features That Make It Stand Out

No Coding Required: Beginners can create models using a graphical interface without touching a single line of code.

Pre-built Algorithms: From regression to classification, users have access to a wide range of built-in machine learning algorithms.

Data Handling: Easily import data from cloud sources, local files, or even real-time streaming services.

Experimentation Workspace: Track, compare, and evaluate different model versions for better results.

Deployment Options: Deploy models as web services for easy integration into applications.

Why Azure ML Studio Is Ideal for Beginners

If you're just stepping into the world of data science, Azure ML Studio is one of the most beginner-friendly platforms available. Here's why:

Hands-On Learning: Learn by doing, not just watching tutorials.

Visual Clarity: Understand complex workflows through intuitive diagrams.

Community and Support: Students can get a lot of help from Microsoft's community forums and documentation.

It’s not just about building models—it’s about understanding how data flows through each stage of the process. This insight is crucial for anyone aspiring to work in data science roles.

A Simple Use Case: Predicting Customer Churn

Imagine you're working with a company that wants to reduce customer churn. Using Azure ML Studio, you can:

Import historical customer data.

Use built-in preprocessing tools to clean and normalize data.

Select a classification algorithm to train your model.

Evaluate the model’s accuracy and fine-tune parameters.

Deploy the model as a web service for real-time predictions.

All of this can be done without writing any code, which is perfect for learners.

Conclusion

Microsoft Azure Machine Learning Studio offers an excellent entry point into the world of data science. It provides the tools, flexibility, and user-friendly interface needed to learn and apply machine learning concepts effectively. As AI continues to shape our world, gaining hands-on experience with platforms like Azure ML Studio can open doors to a wide range of opportunities.

To explore more resources, tutorials, and structured learning paths related to data science and machine learning, check out easyshiksha.com — your partner in building career-ready tech skills.

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Azure AI Vision API for OCR: Text Extraction Techniques Tutorial